Abstract This paper describes a system that uses various cross-language information retrieval (CLIR) methods to provide search engines with capability of automatic search in a local language as well as English even when the user makes search queries only in the local language. It first reviews the CLIR methods that can be used for bilingual search on the Web. Then, it... This paper describes a system that uses various cross-language information retrieval (CLIR) methods to provide search engines with capability of automatic search in a local language as well as English even when the user makes search queries only in the local language. It first reviews the CLIR methods that can be used for bilingual search on the Web. Then, it describes the general process and main components of the system with exemplary case of Korean-to-English conversion of search queries. Finally, it presents the results of an empirical experiment on the system. The results of an empirical experiment on the system demonstrate the usefulness of CLIR methods in converting search queries in Korean into English and delivering search results in English even when the user makes search queries in Korean. The encouraging results suggest a promising future of the system for bilingual search on the Web. While the system is currently for Korean-to-English conversion of search queries, it may be applied to other language sets with some modifications and tailoring specific to each language set. The system would be useful for Web users, expanding the international scope of the Web. We plan to improve and elaborate the system, exploring additional ways that CLIR and similar methods can be exploited and included in the system. Read More Read Less

Abstract Financial crises are perceived as shocking events, several researchers concentrated on the identification of stressed and stable periods in order to take strategic decisions on time. In this paper, for one hand we propose a new hybrid approach to deal with the prediction of the Value at Risk (VaR). Based on financial variables from bank’s balance sheets as input data... Financial crises are perceived as shocking events, several researchers concentrated on the identification of stressed and stable periods in order to take strategic decisions on time. In this paper, for one hand we propose a new hybrid approach to deal with the prediction of the Value at Risk (VaR). Based on financial variables from bank’s balance sheets as input data , this approach integrate Rough Set Theory (RST), Gaussian Case Based Reasoning- clustering (GCBR-Clustering), Real valued Genetic Algorithm (RGA) with Support Vector Machines (SVM) in order to classify stressed and non stressed periods and therefore determine the VaR . The RST-GCBR Clustering-RGA-SVM combination is justified by a high accuracy rate which reaches 96.551% in cluster 1and 100% in cluster 2. In another hand we attempt to highlight the usefulness of our proposed model versus the existing one based on the extreme value theory. Based on a Kupiec backtest it is proved that our proposed approach is more performant at different confidence level. Read More Read Less

Abstract As intimacy in human relationships is getting reduced, social problems increase, and a number of people feel cut off. To support making friends and activating communication, we propose a topic recommendation method for conversation to an appropriate user group. Although previous methods have an issue where many of persons may not enjoy conversation by the topic recommendation method, our proposed... As intimacy in human relationships is getting reduced, social problems increase, and a number of people feel cut off. To support making friends and activating communication, we propose a topic recommendation method for conversation to an appropriate user group. Although previous methods have an issue where many of persons may not enjoy conversation by the topic recommendation method, our proposed method can provide the topics which are preferred by more persons for their conversation, and more persons can enjoy their conversation. The method utilizes the similarities in all combination between topic and a user preference. The topic preference vector which is formed by user as the element and the similarity as the value is constructed for each topic, and the topics are clustered by using the topic preference vectors, based on the similarities. Then, the user groups are extracted from the topic clusters, and the corresponding topics are recommended to the user groups. For the proposed method, the effectiveness of user group construction, the completeness of the user preference extraction and the appropriateness of topic selection are investigated by conducting an simple experiment. As the result, we confirmed the user group construction can help to have more enjoyable conversation, and although the user preference extraction was not enough, the topics offered by the proposed method and along with a category are appropriate for the known persons and the unknown persons, respectively. Read More Read Less

Abstract In 2008, as part of a national agenda of Healthcare reform, the Australian Federal Government commissioned a report into the state of the Australian public health system. The resultant 2009 report by the National Health & Hospitals Reform Commission (NHHRC) described the Australian Health Sector (AHS), and its information and management processes, as “fragmented”. Observed in the NHHRCs depiction of... In 2008, as part of a national agenda of Healthcare reform, the Australian Federal Government commissioned a report into the state of the Australian public health system. The resultant 2009 report by the National Health & Hospitals Reform Commission (NHHRC) described the Australian Health Sector (AHS), and its information and management processes, as “fragmented”. Observed in the NHHRCs depiction of the AHS was that the various levels and services offered within healthcare operate largely arbitrarily, and at multiple local system nodes of a whole larger – albeit fragmented – system. Recommended were numerous strategies designed to ‘fix’ this fragmented complex system. Implied in this depiction and resulting recommendations is the NHHRC’s pre-supposition that systems need to be (traditionally) ‘ordered’ and managed to be considered as functioning in their optimal state. This paper argues that an alternative paradigm, informed by the science of complexity – which conceptualises complex organisations and their processes in terms of being Complex Adaptive Systems (CAS) – is able to provide a more appropriate theoretical foundation for both understanding and facilitating information management within highly complex organisational structures. Chaos and Complexity theories offer – for the organisational theorist – an investigative framework better suited to the dynamic and unpredictable characteristics of information and process management within a complex organisation. In this context, traditional information and management science approaches to understanding organisational characteristics such as fragmentation, process and information duplication, redundancy, and system regulation may require a rethink. Read More Read Less